The challenge of specifying problems (i.e., problem capture) and methods for statistical inference and nonlinear/combinatorial optimization is well known. These challenges include, for example, the automatic derivation of effective inference and optimization algorithms (especially those based on Monte Carlo methods, local and systematic search as well as stochastic variants), as well as hybrids between automatically derived and user-specified algorithms; the automatic transformation and optimization of these algorithms; and the execution of these algorithms either in simulation or natively on commercial off-the-shelf (COTS) (i.e., von Neumann) computers, including massively parallel high-performance computers and Beowulf clusters.